Weakly-Supervised Fine-Grained Event Recognition on Social Media Texts for Disaster Management
Weakly-Supervised Fine-Grained Event Recognition on Social Media Texts for Disaster Management
复制标题
DOI：
10.1609/aaai.v34i01.5391
复制
发表时间：
2020-04
期刊：
影响因子：
--
通讯作者：
Wenlin Yao;Cheng Zhang;S. Saravanan;Ruihong Huang;A. Mostafavi
中科院分区：
文献类型：
--
作者：
Wenlin Yao;Cheng Zhang;S. Saravanan;Ruihong Huang;A. Mostafavi
人们越来越多地使用社交媒体来报告紧急情况、寻求帮助或在灾害期间分享信息，这使得社交网络成为灾害管理的重要工具。为了满足这些时间紧迫的需求，我们提出了一种弱监督的方法，用于快速构建高质量的分类器，这些分类器使用细粒度的事件类别标记每个单独的Twitter消息。最重要的是，我们提出了一种新的方法来创建高质量的标记数据，以及时的方式，自动集群的推文包含一个事件的关键字，并要求一个领域的专家，以消除歧义的事件词义和标签集群迅速。此外，处理非常嘈杂，往往是相当短的用户生成的消息，我们丰富的推文表示使用之前的上下文推文和回复推文在建设事件识别分类。对哈维和佛罗伦萨两次飓风的评估表明，仅使用1-2人-小时的人工监督，快速训练的弱监督分类器的性能优于使用超过50人-小时创建的一万多条注释推文训练的监督分类器。
People increasingly use social media to report emergencies, seek help or share information during disasters, which makes social networks an important tool for disaster management. To meet these time-critical needs, we present a weakly supervised approach for rapidly building high-quality classifiers that label each individual Twitter message with fine-grained event categories. Most importantly, we propose a novel method to create high-quality labeled data in a timely manner that automatically clusters tweets containing an event keyword and asks a domain expert to disambiguate event word senses and label clusters quickly. In addition, to process extremely noisy and often rather short user-generated messages, we enrich tweet representations using preceding context tweets and reply tweets in building event recognition classifiers. The evaluation on two hurricanes, Harvey and Florence, shows that using only 1-2 person-hours of human supervision, the rapidly trained weakly supervised classifiers outperform supervised classifiers trained using more than ten thousand annotated tweets created in over 50 person-hours.